Abstract

A Community Climate System Model, Version 3 (CCSM3) simulation for 125 ka during the Last Interglacial (LIG) is compared to two recent proxy reconstructions to evaluate surface temperature changes from modern times. The dominant forcing change from modern, the orbital forcing, modified the incoming solar insolation at the top of the atmosphere, resulting in large positive anomalies in boreal summer. Greenhouse gas concentrations are similar to those of the pre-industrial (PI) Holocene. CCSM3 simulates an enhanced seasonal cycle over the Northern Hemisphere continents with warming most developed during boreal summer. In addition, year-round warming over the North Atlantic is associated with a seasonal memory of sea ice retreat in CCSM3, which extends the effects of positive summer insolation anomalies on the high-latitude oceans to winter months. The simulated Arctic terrestrial annual warming, though, is much less than the observational evidence, suggesting either missing feedbacks in the simulation and/or interpretation of the proxies. Over Antarctica, CCSM3 cannot reproduce the large LIG warming recorded by the Antarctic ice cores, even with simulations designed to consider observed evidence of early LIG warmth in Southern Ocean and Antarctica records and the possible disintegration of the West Antarctic Ice Sheet. Comparisons with a HadCM3 simulation indicate that sea ice is important for understanding model polar responses. Overall, the models simulate little global annual surface temperature change, while the proxy reconstructions suggest a global annual warming at LIG (as compared to the PI Holocene) of approximately 1°C, though with possible spatial sampling biases. The CCSM3 SRES B1 (low scenario) future projections suggest high-latitude warmth similar to that reconstructed for the LIG may be exceeded before the end of this century.

1. Introduction

Since the start of the industrial age, atmospheric greenhouse gases have increased to levels that the Earth has not experienced for over at least 800 000 years. These increases have changed the Earth's energy balance with an estimated radiative forcing of 1.6 W m−2 [1]. Environmental changes associated with these changes in forcing are detectable and numerous, including a global warming of 0.74°C from 1906 to 2005 and a rate of sea-level rise averaging 1.8 mm yr−1 for 1961–2003 [1]. Projections of future surface temperature changes by AD 2100 range from 1.1 to 6.4°C depending on the emission scenario pathway followed. Uncertainties include the degree of polar amplification of temperatures, which vary among the models used for projections [2]. Yet, warming of the atmosphere, oceans and land in the polar regions has important implications for stability of the Greenland and Antarctic ice sheets, permafrost degradation and associated methane release, and sustainability of the biological diversity.

Over the last million years, the Earth's climate has oscillated between colder, glacial climates and warmer, interglacial climates. These changes were driven by the well-known orbital periods [3], which altered the latitudinal and seasonal incoming solar radiation, with resulting feedbacks of greenhouse gases and ice sheets amplifying the orbital forcing. Some of the past interglacials over the last 500 kyr may have been warmer than today [4–6]. Understanding the forcings and feedbacks that produced interglacial warmth and the outcomes from it can help us better project the future climate of our planet. Climate model simulations allow an assessment of how well models used for projecting future climate can reproduce the evidence from past interglacials.

The Last Interglacial (LIG, from approx. 130 to 116 ka) is a useful target for model–data comparisons for several reasons. Because it is the penultimate interglacial before the Holocene, our present interglacial, more data are available to compare to models than for earlier interglacials. Ocean drilling has retrieved a large number of sediment cores for the LIG [6,8,9]. Ice cores provide records of temperature derived from stable water isotopes that extend through the LIG in Greenland [9] and the last 800 kyr in Antarctica [10,11]. The availability of pollen records from the LIG, particularly at extratropical latitudes in the Northern Hemisphere (NH), allows estimates of temperature owing to the close relationship of plants and climate [12].

These palaeoclimatic records provide geographical patterns of temperature change for comparison to climate model simulations. Early simulations with atmospheric general circulation models and idealized orbital configurations of maximum tilt and eccentricity and perihelion at 21 June simulated significantly warmer NH mid- and high-latitude summers [13]. High-latitude winters were also simulated to be warmer than modern owing to sea ice feedbacks. Atmosphere ocean general circulation models (AOGCMs) with orbital forcings of 125 and 130 ka confirm these patterns of seasonal warmth [14–17]. AOGCMs though have not been able to produce the warming indicated by the East Antarctic ice cores when forced by orbital forcing changes only [17,18]. Transient simulations for the LIG with intermediate complexity models [19] suggest that Arctic warming peaked early in the interglaciation because obliquity peaked earlier than precession [20], while meltwater forcing introduced to the North Atlantic can generate an early Antarctic warming [17].

In this paper, we compare the CCSM3 climate model simulations for 125 ka to two recent data syntheses, assess the parallels and explore the differences. We use the model simulation to inform an interpretation of data seasonality [21–23]. Owing to limitations on the absolute dating of proxy records during the LIG, both data syntheses assessed maximum warmth in the period 135–118 ka and assumed this warmth was broadly synchronous in time. We include comparisons to CCSM3 simulations for 130 and 120 ka to evaluate this assumption. We also evaluate a sensitivity simulation with the West Antarctic Ice Sheet (WAIS) removed to test the impact of its extent being much reduced during the LIG [18,24]. We conclude with a comparison to a HadCM3 125 ka simulation, which gives an indication of the robustness of the temperature responses across models, as well as a comparison to the warmth projected for the end of this century.

2. Model description, experimental design and forcings

We use simulations from a fully coupled, global atmosphere–land surface–ocean–sea ice general circulation model: the Community Climate System Model, v. 3 (CCSM3). Future climate predictions from this model are presented in the IPCC AR4 report [1]. The model has also been used in the Palaeoclimate Modelling Intercomparison Project (PMIP) to simulate Last Glacial Maximum and Mid-Holocene climates [25,26].

The CCSM3 was developed by the US modelling community and is maintained at the National Center for Atmospheric Research (NCAR). We use the T85×1 version of CCSM3 [27] with no flux adjustments. The atmosphere model CAM3 is a three-dimensional primitive equation model solved with the spectral method in the horizontal and with 26 hybrid coordinate levels in the vertical [28]. The T85 spectral resolution corresponds to an equivalent grid spacing of approximately 1.4° in latitude and longitude. The land model CLM3 uses the same grid as the atmospheric model and includes specified but multiple land covers and plant functional types within a grid cell [29]. The ocean model is the NCAR implementation of the Parallel Ocean Program (POP), a three-dimensional primitive equation model in vertical z-coordinate [30]. The ×1 ocean grid has 320×384 points with poles located in Greenland and Antarctica and 40 levels extending to 5.5 km depth. The ocean horizontal resolution corresponds to a nominal grid spacing of approximately 1° in latitude and longitude with greater resolution in the tropics and North Atlantic. The sea ice model uses the same horizontal grid as the ocean model. It is a dynamic–thermodynamic formulation, which includes a subgrid-scale ice thickness distribution and elastic–viscous–plastic rheology [31].

Coupled pre-PMIP3 climate simulations have been completed with the CCSM3 for pre-industrial (PI) conditions and for 125 ka. For the results in this paper, the CCSM3 statistics are calculated from the last 30 years of a 950-year PI simulation (started from an AD 1990 control simulation) and the last 30 years of a 350-year 125 ka simulation (started from a previous LIG simulation with an earlier version of the model CCSM2). Differences between the LIG and PI simulations are evaluated with the Student t-test. The CCSM3 125 ka and PI simulations were run sufficiently long to minimize surface trends. Small trends, less than 0.1°C per century, are still present in the Southern Ocean. The deep ocean is still not in equilibrium with the temperatures at 2.6 km depth, cooling approximately 0.1°C per century in both the 125 ka and PI simulations.

For the 125 ka and PI simulations, we assume present-day geography, Greenland and Antarctic ice sheets and vegetation (table 1). The greenhouse gas concentrations for 125 ka, carbon dioxide (CO2) and methane (CH4), are estimated from ice core measurements [32]. Those for PI are set appropriate for AD 1870 (table 1). The greenhouse gas changes result in a radiative forcing (defined as 125 ka versus PI, and calculated using formulae from the 2001 IPCC report [33]) of −0.36 W m−2. The solar constant in the 125 ka simulation was set to 1367 W m−2, the value used in the CCSM3 present-day simulation to allow comparison to previous simulations done with the earlier CCSM2 model [16]. The solar constant in the PI simulation, on the other hand, was set to 1365 W m−2, as in the CCSM3 PMIP2 simulations. The net effect of the differences in the solar constant and greenhouse gas concentrations results in a net radiative forcing of −0.02 W m−2.

The Earth's orbital configuration during the LIG was different from what it is today and this constitutes the dominant forcing change for 125 ka as compared to modern. These orbital changes are well understood and can be calculated from astronomical equations [3]. The obliquity (tilt of the Earth's axis) with an approximately 41 kyr quasi-periodicity was larger at 125 ka (23.80°) than today (23.44° for AD 1990). Obliquity modulates solar insolation at high latitudes of both hemispheres. Eccentricity, with dominant periodicities of approximately 100 and 400 kyr, was also much larger during the LIG (0.040 at 125 ka as compared to 0.0167 for AD 1990). It serves to modulate the 20 kyr precessional cycle. Perihelion (the closest distance of the Earth to the Sun) took place in late July (boreal summer) at 125 ka but occurs in early January (boreal winter) in AD 1990.

The orbital forcing modifies the incoming solar insolation at the top of the atmosphere (figure 1). The annual changes at 125 ka as compared to today are small, less than a few W m−2. Seasonal changes, on the other hand, are large, with anomalies as big as ±10–15% of average insolation. At 125 ka, June insolation increases by more than 55 W m−2 at high northern latitudes and mean May–June–July insolation anomalies at 65°N are approximately 20% greater than in the Early Holocene [23]. This increase is partially compensated during the boreal winter such that annual changes are less than 3 W m−2 at these latitudes. Summer insolation in the Southern Hemisphere (SH) was reduced relative to PI.

3. Datasets

We compare the climate model results to two recent LIG data syntheses [7,8]. Both compilations are based on published records with quantitative estimates of mean annual surface temperature change. In the marine-only dataset of McKay et al. [8], seasonal anomalies are also available with the overall pattern similar to the annual anomalies. The only available synthesis over land is for annual anomalies [7].

The Turney & Jones [7] global dataset is made up of 263 published records that span the LIG and have quantitative estimates of mean annual surface temperature. Three ice core temperature estimates from δ18O are included from Greenland and four from East Antarctica. Marine records of mean annual sea surface temperature (SST) include those obtained from foraminifera, radiolarian and diatom transfer functions and calibrations using Mg/Ca and Sr/Ca ratios and alkenone unsaturation indices (i.e. ). Absolute dating of LIG proxy records is difficult. Because of this, Turney & Jones average the temperature estimates across the isotopic plateau associated with the LIG in the marine and ice core records. The terrestrial mean annual surface temperature estimates are based on the original interpretations from pollen, macrofossils and Coleoptera, taking the period of maximum warmth and assuming that this warmth is broadly synchronous with the marine and ice core plateaus. The pollen and macrofossil records are converted to quantitative temperature estimates in the original publications using a variety of methods, including modern analogue, regressions and other inversions. Annual temperature changes are calculated from modern using the present-day mean annual temperatures (MATs) at each palaeosite location using the 1961–1990 CRU dataset [34] for terrestrial sites and ESRL dataset [35] for ocean sites.

The McKay et al. [8] global dataset is a compilation of 76 records from published palaeoceanographic sites. The annual SST estimates are from Mg/Ca in foraminifera, alkenone unsaturation ratios and faunal assemblage transfer functions for radiolaria, foraminifera, diatoms and coccoliths. Only records with published age models and an average temporal resolution of 3 kyr or better for the LIG and Late Holocene were included. Because of dating uncertainties, McKay et al. average the SST estimates for a 5000-year period centred on the warmest temperatures between 135 and 118 ka at each site. SST changes are calculated from the Late Holocene (last 5 kyr). This compilation was additionally supplemented with the 94 CLIMAP Project LIG SST change from core-top values.

The combined datasets, which include some overlap over the oceans, give a broadly consistent global synthesis of global MAT change (figure 2). Annual surface temperatures were warmer than modern at mid- and high latitudes of both hemispheres. The data indicate strong warming in the northern and southern polar regions, generally greater than 4–5°C north of 60°N over land and ocean, and 2–5°C for the Antarctic ice cores. The very large warming over northern Asia and Alaska is based on pollen and plant macrofossils and may reflect a bias towards summer warming [23]. A less consistent picture emerges for temperature changes in the tropics. Coastal upwelling regions, particularly along the California coast and the African coast south of the equator, indicate warming. The rest of the tropical Atlantic Ocean was cooler during the LIG, whereas the eastern tropical Pacific Ocean has adjacent cores suggesting warming and cooling. The data coverage is good for the Atlantic Ocean and eastern Pacific basin, but poor over the rest of the Pacific Ocean, the Indian and Southern Oceans and in the continental interiors. Analysis of McKay et al. suggests a peak LIG global annual SST warming of 0.7±0.6°C as compared to the Late Holocene. Turney & Jones suggest a peak LIG annual surface temperature warming (land + ocean) of 1.5±0.1°C compared to an AD 1961–1990 baseline or approximately 1.9°C warmer than PI [35].

Reconstructed mean annual surface temperature (MAT) change for LIG from modern as reconstructed by Turney & Jones [7] and McKay et al. [8]. See text for description of methods.

4. Simulated surface temperatures

(a) Mean annual surface temperatures

The simulated MAT change at 125 ka relative to PI shows significant warming at high and mid-latitudes of the NH (figure 3). This warming is greatest over the North Atlantic south of Greenland (in excess of 4°C) and in agreement with proxy estimates of SST anomalies ranging from 2.7 to 5°C. The model also simulates warming farther north in the Greenland–Iceland–Norwegian Seas, though a comparison with the data is less straightforward because of wide disparity among the proxy estimates of SST change. Warming over North America and Eurasia is greatest in the western portions of these continents and decreases eastward to slight warming or even slight cooling. The model simulates warmer SSTs off the coasts of California and Spain, in good agreement with the data.

CCSM3-simulated mean annual surface temperature change for 125 ka minus PI overlain by reconstructed MAT changes. The surface temperatures are limited to the minimum ocean freezing temperature of −1.8°C over the ocean and sea ice covered regions to compare with the SST proxy data. White regions indicate sea ice in both the 125 ka and PI simulations. Differences significant at less than 95% using the Student t-test are stippled.

The model underestimates the proxy indications of warming for the far northern coastal regions of Alaska (simulated: 0.5–1°C versus data: 5.5–6.7°C) and Siberia (simulated: 1–2°C versus data: 7.1–14.8°C). A possible reason is that the models do not include vegetation feedbacks [16, supplementary material, 36], which could be important in these high-latitude regions (see §5). The simulated surface temperature anomalies at the summit of Greenland are 2.2°C as compared to NGRIP and GRIP stable water isotope estimates of annual warming of 5°C [37,38]. Some of the observed warming may be associated with reduced elevation of the central Greenland ice sheet [9,39].

CCSM3 also simulates mean annual warming over the SH subtropical landmasses of South America, Africa and Australia, but lack of terrestrial proxies in the data syntheses for these regions does not allow evaluation of the model. Slightly cooler MATs are simulated for Antarctica in contrast to East Antarctic ice core records that indicate substantial warming of 1.5–4.5°C [10,32,40,41]. Simulated cooler MATs over North Africa and southern Asia are consistent with the simulated enhanced summer monsoons. Previous modelling studies [42] and palaeorecords [43–45] also confirm the relationship between increased seasonality of the insolation in the NH and expansion/intensification of these monsoon systems.

Simulated SST changes south of 30°N are small, generally within ±1°C of the PI control simulation. The data, on the other hand, show more regionally variable anomalies. Coastal upwelling regions record much warmer SSTs not simulated by the model. Coastal upwelling regions are difficult for climate models to resolve and simulate well [46]. The reconstructions show SST cooling in excess of 2°C over the tropical Atlantic and Indian Oceans, though with limited data coverage in the latter. Simulated changes in these ocean basins are of the correct sign but underestimate the magnitude of the observed cooling. For the Southern Ocean, the data suggest larger SST anomalies than the model, although with significant regional heterogeneity.

CCSM3 shows strong polar amplification of MAT north of approximately 45°N with approximately similar warming over the ice-free oceans and continents (figure 4). This is associated with a seasonal memory of sea ice retreat in CCSM3 that extends the effects of positive summer insolation anomalies on the high-latitude oceans to winter months, affecting the North Atlantic, Arctic Ocean and adjacent continents. Over mid-latitude continental regions, the strong summer warming dominates the annual average. South of 45°N, CCSM3 simulates zonally averaged MAT close to zero or slightly negative, with stronger continental cooling at subtropical northern latitudes associated with the African and Asian monsoons (figure 4).

While the proxy data suggest significant regional heterogeneity in the MAT change in the tropics and SH extratropics, the simulated response in CCSM3 is relatively uniform with no change to weak annual surface cooling at the locations of the data (figure 5). The proxy data suggest an average annual warming in the tropics (30° N–30° S) of approximately 0.3°C and at SH extratropical latitudes (30–90° S) of approximately 1.2°C, while CCSM3 simulates weak annual cooling of approximately 0.3–0.5°C in both regions (table 2). The proxy data also indicate that the average annual warming over the continents was about twice that over the oceans in the tropics, a feature that CCSM3 does not capture (table 2).

CCSM3 warms the NH extratropical (30–90°N) continents more than oceans, with simulated warming at the palaeo-data locations approximately 1.0°C over the continents and approximately 0.5°C over the oceans (table 2), though with much more regional heterogeneity in the simulated SST anomalies than terrestrial MAT anomalies (figure 5). The proxy data, on the other hand, suggest similar and larger annual warming of approximately 1.7°C for the terrestrial and ocean proxies. The full-grid averages from CCSM3 also show approximately 0.5°C more warming over the NH extratropical continents than oceans, suggesting that the lack of contrast in the data is not a reflection of the uneven data distribution.

Globally, a simple point-by-point average of the combined proxy reconstructions indicates an annual warming of 0.98°C, with the continents warming by 1.67°C and the oceans warming by 0.76°C. CCSM3 simulates no change of mean annual surface temperatures at 125 ka as compared to PI (table 2). Simulated terrestrial temperatures are globally warmer by 0.92°C when sampled at the proxy locations but show no change when averaged over all model land points. The terrestrial reconstructions are strongly biased towards middle and high latitudes, with no data included in these reconstructions in the monsoon regions in which CCSM3 simulates cooling. These results suggest that the spatial sampling of the reconstructions could introduce a strong bias in our perception of the LIG: the model can simulate warming at terrestrial proxy sites, on average, and at the same time simulate no average change in the ‘true’ model global temperatures.

(b) Seasonality of model response

Uncertainties exist in the reconstructions in the seasonality of biological proxies, which may be biased systematically towards specific seasons. Terrestrial quantitative reconstructions are often derived from sediments indicating a change in the geographical ranges of specific plants. The biotic dominance of high-latitude vegetation is influenced by temperature, growing season length and moisture availability [47]. Seasonality is well constrained for Europe but less so for other regions [15,23]. The primary SST proxies are also sensitive to changes in seasonality [48,49]. SSTs derived from foraminifera Mg/Ca are known to reflect the calcification temperature of the species, which is best represented by the warm season conditions [50]. Environmental preferences of alkenone-producing algae may bias their proxy data estimates towards warmer temperatures, particularly in regions affected by both upwelling and open ocean conditions [51]. Locally, their signal can also be strongly seasonal [52,53].

The simulated surface temperatures for 125 ka show a strong seasonality of the surface temperature response (figure 6) consistent with the solar insolation anomalies (figure 1). The boreal summer (JJA) warming is larger and more extensive over the NH landmasses than in the annual mean. Poleward of approximately 30° N, surface temperature anomalies are greater than 2°C over almost all of North America, Eurasia and Greenland, and exceed 5°C over the interiors of Europe, Asia and North America. In Siberia along the Arctic coast, simulated JJA surface temperature anomalies are 2–3°C greater than the annual anomalies but only 0.5 to 1°C greater in northern Alaska. In both regions, the JJA simulation shows better correspondence with the MAT reconstruction but still underestimates the proxy indications of very strong LIG MAT warming (figure 7). The SH continental regions located at tropical and subtropical latitudes also show enhanced warming in JJA. This is not unexpected as the positive JJA insolation anomalies extend into the tropics and SH. Antarctica shows warming in JJA in CCSM3. The simulated JJA surface temperature anomalies compare much better to the calibrated MAT data estimates over the NH extratropics than do the simulated MAT anomalies though CCSM3 cannot capture the large warming indicated by some terrestrial proxies (figure 7). Over the SH extratropics and tropics, the simulated JJA surface temperature changes are only modestly warmer than the simulated MAT changes.

The austral summer (DJF) simulated surface temperature anomalies are cooler in many regions, with less correspondence with the MAT-calibrated data (figures 6 and 7). Only over high northern latitudes are there substantial warm anomalies. These warm anomalies occur over the North Atlantic and to a lesser extent over Canada and Europe. They cannot be explained by local insolation; the DJF insolation anomalies at 125 ka as compared to PI are negative at these latitudes. Rather they reflect the seasonal memory of surface temperature to the reduced sea ice and snow extent associated with the boreal summer insolation anomalies. Over northern Eurasia the simulated warming (though not significant) agrees with the expectation from proxy records of warmer winters at LIG than PI [12] but is underestimated in terms of magnitude or eastward extent. The colder SSTs simulated in the subtropical Indian Ocean in austral summer agree better with the proxy records. The lack of any improved agreement between the simulations and the data in upwelling regions suggests that seasonality is not an important consideration for the data–model mismatch.

(c) Early, middle and late LIG simulations

Because of difficulties in absolute dating for the LIG, both data syntheses assess maximum warmth (135–118 ka) and assumed this warmth was broadly synchronous in time. The seasonal and latitudinal natures of the insolation anomalies, though, change throughout the LIG. The obliquity reached a maximum of 24.24° at 130 ka, remained relatively high at 23.80° at 125 ka and decreased to 23.01° at 120 ka. Obliquity affects both seasonal contrasts and annual solar insolation, with the largest anomalies at high latitudes of both hemispheres. Annual insolation anomalies as compared to present reach close to 6 W m−2 at the poles for 130 ka, remain positive but decrease to approximately 3 W m−2 for 125 ka and become a negative forcing annually at high latitudes for 120 ka. Perihelion shifted from early May at 130 ka to late July at 125 ka to mid-September at 120 ka, with important impacts on NH insolation. Seasonal changes in insolation associated with precessional forcing are strong during the entire LIG because of the large eccentricity throughout the LIG.

The orbital changes result in seasonal and latitudinal changes in insolation that evolve over the LIG (figure 1). At 130 ka, the largest positive insolation anomalies in the NH occur in May and June and exceed 50 W m−2 from the North Pole to NH subtropical latitudes. By 120 ka, the positive NH insolation anomalies have shifted to August and September and are much weaker.

Greenhouse gas concentrations used in the CCSM3 simulations are different for the three LIG time periods, contributing an additional radiative forcing of about 0.6 W m−2 at 130 ka as compared to that at 125 ka, or potentially approximately 0.4°C of global annual mean warming. It should be noted that the greenhouse gas concentrations used for 130 ka are higher than the Dome C reconstruction followed in the PMIP3 LIG protocols [54]. Rather the CCSM3 130 ka simulation is more appropriate for 128 ka, which had June insolation anomalies similar to 130 ka and atmospheric CO2 and CH4 measurements suggesting concentrations of 287 ppmv and 724 ppbv, respectively. Differences in the radiative forcings associated with modest changes in the greenhouse gas concentrations between 125 and 120 ka are small.

Globally CCSM3 is warmer at 130 ka than at 125 ka (tables 2 and 3). CCSM3 exhibits larger warming at 130 ka than at 125 ka over the North Atlantic Ocean, Ellesmere Island and Greenland (figures 3 and 8). The summit of Greenland warms by 3 to 3.5°C at 130 ka as compared to PI, but still less than the 5°C warming indicated by the GRIP and NGRIP ice cores. The simulated greater warming at 130 ka than at 125 ka for the eastern Canadian Arctic is consistent with evidence that the transition into the LIG was rapid, with peak summer warmth early in the LIG [23]. Warming extends farther eastward over the mid- and high-latitude continental regions of North America and Eurasia at 130 ka than at 125 ka, improving but still underestimating the proxy evidence of surface temperature changes. Overall, the NH extratropics warm by 1.04°C at 130 ka (as compared to 0.76°C at 125 ka) in CCSM3 when sampled at the proxy locations and 0.71°C at 130 ka (as compared to 0.27°C at 125 ka) when averaged for the full grid (tables 2 and 3).

As in figure 3 for CCSM3 (a) 130 and (b) 120 ka simulations as compared to PI simulation. White regions and shading as in figure 3.

The Antarctic surface temperature anomalies change from a slight cooling at 125 ka to a slight warming at 130 ka but still cannot explain the observed warming of the ice cores. Early LIG warmth in the Southern Ocean and Antarctic is consistent with East Antarctic ice core and Southern Ocean marine records [10,55]. SH extratropical and tropical simulated cooling is significantly reduced in the 130 ka simulation as compared to the 125 ka simulation, but both regions remain cooler than PI.

Late in the interglaciation at 120 ka, the solar insolation anomalies associated with orbital changes result in simulated MAT similar to PI, with anomalies generally less than ±1°C and not significantly different from the PI control. Globally, the MAT change simulated by CCSM3 is +0.10°C at 130 ka with significant warming at mid- and high latitudes in the NH and some warming over Antarctica, −0.16°C at 125 ka with significant warming at mid- and high latitudes in the NH but strong cooling in NH monsoon regions and −0.14°C at 120 ka with little significant temperature change anywhere as compared to PI.

(d) Vegetation and polar ice sheets

A possible explanation of model–data mismatches in some regions is that not all appropriate changes in the boundary conditions have been considered in the design of the experiments. The LIG simulations with CCSM3 assumed modern vegetation. Northern Alaska and northern Siberia are covered with tundra in these simulations. The pollen and macrofossil evidence for the LIG indicates boreal forests extending to the Arctic Ocean coastline everywhere except in Alaska and central Canada [56,57]. Boreal forests have a lower albedo than tundra and can also partially mask snow allowing for more absorption of incoming solar radiation and warming. Deciduous broad-leaf forests can also contribute to greenhouse warming owing to enhanced transpiration of water vapour to the atmosphere [58]. Some of the underestimation of proxy-inferred warming may therefore be a consequence of not including vegetation feedbacks. Previous modelling of the LIG has shown that the feedback between vegetation and climate can enhance the warming at high latitudes [36,59].

Global sea level was likely 4–9 m higher during the LIG relative to present day [16,18,60,61]. A sea-level rise of up to 4 m during this time interval has been attributed to some Greenland ice sheet and other Arctic ice fields melting in combination with ocean thermal expansion [8,16]. A Greenland ice sheet contribution above 4 m is discredited by evidence of the presence of ice in the summit cores dating to before the LIG as well as Greenland ice sheet simulations that show if the ice sheet is completely removed, warming is an additional 10°C in disagreement with Greenland ice records [16]. Any sea-level rise above approximately 4 m during the LIG must then have come from the WAIS [18] and possibly the East Antarctic Ice Sheet. WAIS is largely grounded below sea level. The WAIS plays a role in buttressing the ice sheet and is particularly sensitive to ocean temperatures and circulation. Large WAIS glaciers near the grounding line show large basal melt rates as ocean waters bathing the edges of these glaciers warm [62]. Ice sheet/ice shelf models for Antarctica demonstrate brief but dramatic interglacial retreats of the WAIS, taking one to a few thousand years, for sub-ice melting of 2 m yr−1 under the shelf interior [63]. Benthic foraminifera data from cores in the North Atlantic and Southern Ocean and climate simulations indicate that warm SST in the North Atlantic could have been transported to circumpolar deep water around Antarctica [64]. Direct geological evidence for a retreat of this ice sheet though remains equivocal.

A possible explanation then for the LIG simulation mismatches as compared to the Antarctic ice cores is the assumption of the presence of the WAIS in the model design. To test this sensitivity, we removed the WAIS in the CCSM3 130 ka simulation replacing it with ocean of depths up to 2000 m. Additional significant warming is restricted to Antarctica in this simulation. CCSM3 warms the region of the WAIS by more than 10°C (figure 9). It also enhances the MAT warming over East Antarctica but only by a few tenths °C, still greatly underestimating the warming indicated by the ice cores. Simulations with HadCM3 [17] suggest that in addition to the WAIS retreat, freshwater input to the North Atlantic from the Laurentide and Eurasian ice sheets during the termination is needed to produce MAT warming that is consistent with the ice core data. This is consistent with evidence of persistent iceberg melting at high northern latitudes at the beginning of the LIG [55].

The HadCM3 model was developed at the Hadley Centre for Climate Prediction and Research [65] and, similar to CCSM3, it does not include flux corrections [66]. The atmosphere and ocean models are grid-point, primitive equation models. The resolutions of the atmospheric and land components are 3.75° in longitude by 2.5° in latitude, with 19 vertical levels in the atmosphere. The resolution of the ocean model is 1.25° by 1.25° with 20 levels in the vertical. The land surface scheme is MOSES 2.1, and the sea ice model uses a simple thermodynamic scheme and contains parametrizations of ice concentration [67] and ice drift and leads [68].

The CCSM3 and HadCM3 simulations were performed independently by each group so the experimental designs are similar but not identical, as they will be in the more formal model intercomparison project. For the 125 ka and PI simulations, both models assume 125 ka orbital forcing and present-day geography, Greenland and Antarctic ice sheets and vegetation. The solar constant and greenhouse gas concentrations are slightly different for each model but result in very similar net radiative forcing (−0.02 W m−2 for CCSM3 and +0.01 W m−2 for HadCM3). The climate sensitivities for a doubling of CO2 are roughly comparable, being 2.7°C for CCSM3 and 3.2°C for HadCM3. The HadCM3 statistics are calculated from the last 50 years of a 550-year 125 ka simulation, itself started from a PI simulation of over 1000 years in length. HadCM3 was run sufficiently long to eliminate significant surface trends though trends in the deep ocean are still present.

Globally, HadCM3 simulates a warmer mean annual climate than CCSM3, with global MAT change as compared to the PI of +0.14°C for HadCM3 and −0.16°C for CCSM3, as calculated on the model grids (tables 2 and 4). Calculated only at the data locations, HadCM3 simulates a global warming for 125 ka of 0.27°C versus 0.10°C for CCSM3. The annual warming in both models is significantly less than the value of 0.98°C calculated from the combined reconstructions.

Robust features simulated by both of the models are the warming over central North America, Europe, South Africa and Australia, and the cooling extending from North Africa to India and Southeast Asia (figures 3 and 10). The models disagree in their responses over Greenland and the North Atlantic, with the HadCM3 model simulating little MAT change and CCSM3 better reproducing the large warming indicated by the reconstructions. Neither model can simulate the strong warming suggested by terrestrial data along the coastal Arctic Ocean. For the SH extratropics, HadCM3 simulates MAT approximately 0.5–1°C warmer than CCSM3, with the warming over East Antarctica simulated by HadCM3 agreeing in sign with the ice core data. However, HadCM3 cannot reproduce the 1.5–4.5°C MAT warming reconstructed from the East Antarctic ice cores. In the tropics, both models simulate only small MAT changes and neither can reproduce the much warmer SSTs in the coastal upwelling regions.

CCSM3 and HadCM3 differ markedly in the magnitude and even sign of polar amplification of MAT. CCSM3 shows strong polar amplification of MAT north of approximately 45°N over the oceans and continents (figure 4). South of 45° N, CCSM3 simulates zonally averaged MAT close to zero or slightly negative. HadCM3 exhibits much reduced polar amplification of the MAT and a more symmetric response with small warming at both poles. Differences between the two models may be related to their sea ice sensitivities (figures 3 and 10). Both models simulate strong warming at mid- to high latitudes of the NH in JJA (not shown) consistent with the positive insolation anomalies but only CCSM3 retains memory of this warming in the North Atlantic and adjacent land regions. HadCM3 with less winter (DJF) sea ice south of Greenland at PI shows less sensitivity. In the SH, HadCM3 has less sea ice off East Antarctica than CCSM3 (white areas in figures 3 and 10) allowing the nearby Southern Ocean and East Antarctica to warm in response to the positive anomalies in austral winter and spring. These results are consistent with the contrasting Arctic and Antarctic sea ice extent sensitivities in the late twentieth century simulations in these two models [69].

6. Summary and implications

CCSM3 simulations for the LIG (approx. 130–116 ka) are compared to the two recent data syntheses of Turney & Jones [7] and McKay et al. [8]. The dominant forcing change for the LIG is the large seasonal changes in the incoming solar radiation associated with orbital forcing. The model responds with substantial annual warming at 125 ka for high and mid-latitudes of the NH, although underestimates the proxy indications of this warming. Over Antarctica, the model simulates cooling as compared to large LIG warming recorded by the Antarctic ice cores. CCSM3 simulates an enhanced seasonal cycle over the high-latitude continents of both hemispheres with simulated JJA warming in better correspondence with the reconstructed annual temperature changes. The observed warming was not likely globally synchronous, with the Antarctic ice cores and records from the Southern Ocean indicating early LIG warmth [10,55]. CCSM3 shows better agreement with the SH records when forced with 130 ka insolation anomalies than the simulation for 125 ka, though the simulated warming is small. Further assessment of the seasonality of the proxy records and improvements in dating of proxy records, when possible, will be important for further assessing how well models can simulate the feedbacks in response to orbital forcing.

Some of the model–data discrepancy may be associated with the experimental design, with CCSM3 adopting present-day vegetation and polar ice sheets, boundary conditions that data and previous sensitivity simulations indicate may explain a portion of the data–model differences. A CCSM3 sensitivity simulation with the removal of the WAIS provides additional local warming over Antarctica but still not enough to explain the ice core records. Simulations with HadCM3 [17] suggest that in addition to the WAIS retreat, freshwater input to the North Atlantic from the Laurentide and Eurasian ice sheets during the termination is needed to produce Antarctic warming that is consistent with the ice core data. A bipolar response is consistent with evidence of persistent iceberg melting at high northern latitudes at the beginning of the LIG [55].

The main series of simulations presented in this paper are from one model, CCSM3. A similar experiment for 125 ka has been completed with HadCM3. This allows an assessment of robustness of the CCSM3 simulation. Both model simulations suggest little global annual surface temperature change at 125 ka as compared to PI, when averages are computed for the full model grids. On the other hand, both show small global warming when averaged over the data locations. Neither simulates a global warming of approximately 1°C suggested by a simple averaging of the proxy data, although this proxy estimate may be influenced by the lack of good spatial coverage of the data. CCSM3 and HadCM3 differ markedly in the magnitude and even sign of polar amplification of MAT, a feature influenced by their sea ice sensitivities. CCSM3 with a strong sensitivity of NH sea ice [69] simulates strong Arctic warming. HadCM3 exhibits much reduced polar amplification of the Arctic MAT and a more symmetric response with small warming at both poles. The similarities and differences in the responses in CCSM3 and HadCM3 point to the need for the LIG model intercomparison project now implemented within PMIP3 [54].

It is interesting to consider the polar warmth indicated by Turney & Jones [7] and McKay et al. [8] reconstructions in comparison to future projection simulations completed by CCSM3 and included in the IPCC AR4 WG1 [70]. The global surface annual warmings projected by CCSM3 for the first few decades of the twenty-first century for the different SRES scenarios track each other closely. The low estimate climate change scenario (SRES B1) peaks in emissions in the mid-twenty-first century with greenhouse gas concentrations starting to level off in the second half of this century. By the end of the twenty-first century, CCSM3 projects a global mean annual warming of 0.9°C for the SRES B1 scenario [70], comparable to that of the LIG reconstructions, with greater warming at high latitudes than at low latitudes (figure 11). East Antarctica and much of the North Atlantic have warmed up to surface temperatures comparable to that of the LIG proxy records. The SRES B1 warming over Greenland is less than the LIG warmth, though, as for the CCSM3 LIG simulations, the CCSM3 future projections fix the Greenland ice sheet heights and extent at present day. Differences in the primary forcings, orbital insolation changes for the LIG versus greenhouse gas concentrations in the SRES projections, give different seasonal responses. While the 125 ka CCSM3 simulation shows the largest warming in JJA, the SRES B1 future projection simulation has the greatest high-latitude warming during the respective winters of the two hemispheres. As such, it would be wrong to consider the LIG as an exact ‘analogue’ for future climate change.

CCSM3-simulated mean (a) annual, (b) December–January–February, and (c) June–July–August surface temperature change for 2080–2099 minus 1980–1999 in the SRES B1 low estimate climate change scenario. The annual surface changes in (a) are overlain with the reconstructed LIG MAT changes. White regions indicate sea ice in both the simulated results for the last two decades of the twentieth and twenty-first centuries.

In summary, there is no clear answer to the question posed in the title ‘How warm was the Last Interglacial?’ Our results show model–data inconsistencies that are not fully understood. The models' sensitivity to the forcings may be too small. Our interpretation of the reconstructed climate parameters may not adequately incorporate seasonal and depth effects and age uncertainties. The implications for future warming scenarios require progress in resolving these inconsistencies between the model simulations and data reconstructions of past interglacials.

Funding statement

The CCSM project is supported by the US National Science Foundation (NSF) and the Office of Science (BER) of the US Department of Energy. NCAR is sponsored by NSF. E.J.S. is funded by the EU project Past4Future. This is Past4Future contribution no. 33. The research leading to these results has received funding from the European Union's Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 243908, ‘Past4Future. Climate change: learning from the past climate’.

Acknowledgements

This research was enabled by the computing resources of the Climate Simulation Laboratory.

References

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